Predictive Models for Opioid Use Disorder Using Genomic, Social, and Clinical Factors

使用基因组、社会和临床因素的阿片类药物使用障碍的预测模型

• 批准号: 10797165
• 负责人: Rodney Allanigue Gabriel
• 金额: $ 19.75万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10797165
• 关键词: Adult; Affect; Age; Body mass index; Chronic; Classification; Clinical; Clinical Data; Code; Communities; Control Groups; Copy Number Polymorphism; Data; Data Set; Databases; Dependence; Development; Diagnosis; Enrollment; Ensure; Equity; Ethnic Origin; Exposure to; Family; Fibromyalgia; Future; Gender; Genetic Markers; Genomics; Geography; Individual; Machine Learning; Medical; Metadata; Methods; Minority; Modeling; Outcome; Output; Pain; Patients; Performance; Persons; Pharmaceutical Preparations; Population; Population Heterogeneity; Populations at Risk; Postoperative Pain; Prevalence; Procedures; Public Health; Race; Recording of previous events; Research; Risk; Sampling; Single Nucleotide Polymorphism; Social Environment; Socioeconomic Status; Substance Use Disorder; Techniques; Translations; Underrepresented Populations; United States; Variant; Visualization; addiction; chronic pain; chronic painful condition; clinical care; clinical decision support; clinically relevant; genome sequencing; genome wide association study; genomic biomarker; genomic data; genomic profiles; health equality; health equity; high risk; illicit opioid; improved; insight; interest; machine learning model; machine learning prediction; multiple data types; neural network; novel; novel marker; opioid abuse; opioid epidemic; opioid mortality; opioid therapy; opioid use; opioid use disorder; outcome prediction; painful neuropathy; patient population; patient stratification; predictive modeling; prescription opioid; prescription pain reliever; risk prediction model; risk stratification; sex; social; social factors; time use; tool; vector; whole genome

PROJECT SUMMARY / ABSTRACT The opioid crisis is a major public health problem in the United States. Over the past two decades, opioid use and abuse have increased dramatically, with over 5 million people in the United States using prescription analgesics without medical need or prescription. This has resulted in a significant increase in opioid-related deaths and addiction rates, with the crisis having a profound impact on individuals, families, and communities. The proposal aims to develop machine learning-based predictive models for opioid use disorder (OUD) leveraging genomic, social, and clinical factors. The project will utilize the diverse and equitable AllOfUs database to identify novel genomic markers associated with OUD in patients with and without co-existing pain conditions. A significant advantage of the AllOfUs database is the diversity of the patient population and clinical samples – over 50% of the population is considered underrepresented. This will be achieved through genome- wide association analysis to identify novel single nucleotide variants, copy number variants, and/or structural variants. The project will also use machine learning techniques to develop predictive models that classify the risk of OUD, integrating various data types such as clinical factors, social factors, and genomic data. The project aims to identify key features that aid in the development of improved models for predicting the risk of OUD. The first specific aim of the proposal is to identify associations between genomic profiles and OUD. The project will focus on patients with or without co-existing pain conditions and identify novel genetic markers associated with OUD in each of these unique patient populations. The second specific aim is to develop predictive models using machine learning techniques to classify the risk of OUD. The models will integrate social, clinical, and genomic data to provide clinicians with a tool to risk stratify their patients. The project aims to develop robust machine learning-based models predicting OUD and visualize the individual features' impacts on model performance to provide understanding of which factors are most impactful to predicting the outcome.

项目摘要/摘要 阿片类药物危机是美国的一个主要公共卫生问题。在过去的二十年里，阿片类药物的使用 滥用情况急剧增加，美国有超过500万人使用处方药 没有医疗需要或处方的止痛药。这导致了与阿片类药物相关的显著增加 死亡率和成瘾率，这场危机对个人、家庭和社区产生了深远的影响。 该提案旨在开发基于机器学习的阿片类药物使用障碍(OUD)预测模型 利用基因组、社会和临床因素。该项目将利用多样化和公平的AllOfU 数据库以确定与伴有和不伴有疼痛的患者的OUD相关的新的基因组标记 条件。AllOfus数据库的一个显著优势是患者群体和临床的多样性 样本--超过50%的人口被认为代表性不足。这将通过基因组来实现- 广泛的关联分析以确定新的单核苷酸变体、拷贝数变体和/或结构 变种。该项目还将使用机器学习技术来开发对风险进行分类的预测模型 集成了各种数据类型，如临床因素、社会因素和基因组数据。该项目 旨在确定有助于开发用于预测OUD风险的改进模型的关键特征。 该提案的第一个具体目标是确定基因组图谱和OUD之间的联系。该项目 将重点放在有或没有共存疼痛状况的患者身上，并确定相关的新遗传标记 在这些独特的患者群体中，每个人都有OUD。 第二个具体目标是使用机器学习技术开发预测模型来对风险进行分类 乌德的。这些模型将整合社会、临床和基因组数据，为临床医生提供风险分层的工具 他们的病人。 该项目旨在开发稳健的基于机器学习的模型，预测OUD并将个人可视化 功能对模型性能的影响，以了解哪些因素对 预测结果。